Design and simulation of an interdigital-chaotic advection micromixer for lab-on-a-chip applications

This paper presents the design and simulation of a novel passive micromixer. The micromixer consists of two inlet tanks, one mixing channel and two outlet channels. In order to maximise the mixing efficiency, the following considerations are made: (i) The inlet tanks are followed by a series of microchannels, in which the flow is split. The microchannels are arranged in an interdigital manner to maximise the contact area between the two flows. (ii) The microchannels attached to the lower inlet tank have an upward slope while those attached to the upper tank have a downward slope. The higher-density flow is fed to the lower inlet tank and gets an upward velocity before entering the mixing channel. (iii) Two triangular barriers are placed within the mixing channel to impose chaotic advection and perturb the less-mixed flow along the top and bottom surfaces of the channel. (iv) Finally, two outlet channels are incorporated to discard the less-mixed flow. Three-dimensional simulations are carried out to evaluate the performance of the micromixer. Simulations are performed in the absence and presence of the gravitational force to analyse the influence of gravity on the micromixer. Mixing efficiencies of greater than 92% are achieved using water and a 1011'density biological solvent as the mixing fluids.

Design and simulation of an interdigital-chaotic advection micromixer for lab-on-a-chip applications

The paper presents the design and simulation of a novel passive micromixer. The micromixer consists of two inlet tanks, one mixing channel and two outlet channels. In order to maximise the mixing efficiency, the following considerations are made : (i) The inlet tanks are followed by a series of microchannels, in which the flow is split. The microchannels are arranged in an interdigital manner to  maximise the contact area between the two flows. (ii) The microchannels attached to the lower inlet tank have an upward slope while those attached to the upper tank have a downward slope. The higher-density flow is fed to the lower inlet tank and gets an upward velocity before entering the mixing channel. (iii) Two triangular barriers are placed within the mixing channel to impose chaotic advection and perturb the less-mixed flow along the top and bottom surfaces of the channel. (iv) Finally, two outlet channels are incorporated to discard the less-mixed flow. Three-dimensional simulations are carried out to evaluate the performance of the micromixer. Simulations are performed in the absence and presence of the gravitational force to analyse the influence of gravity on the micromixer. Mixing efficiencies of greater than 92 % are achieved using water and a low-density biological solvent as the mixing fluids.

Lack of genetic divergence found with microsatellite DNA markers in the tarakihi Nemadactylus macropterus

Three classes of molecular markers are commonly employed during population genetic studies of marine taxa: allozymes, mitochondrial DNA (mtDNA), and microsatellite DNA. These markers differ in their levels of polymorphism, and the ease and cost of their application. Nemadactylus macropterus is a commercially important marine fish from New Zealand and southern Australia that has been the subject of genetic (allozyme, mtDNA) and non-genetic (otolith microchemistry, larval advection) studies of stock structure. We collected microsatellite DNA data from this species to compare the utility of these molecular markers with those genetic methods previously applied to N. macropterus. Microsatellites did not indicate significant divergence among Australian samples, or between Australian and New Zealand samples. The latter is incongruent with the allozyme and mtDNA studies, and it is suggested that allelic homoplasy has hindered the resolution of population structure when using microsatellites.

Modelling the fate of chromated copper arsenate in a sandy soil

A pulse of chromated copper arsenate (CCA, a timber preservative) was applied in irrigation water to an undisturbed field soil in a laboratory column. Concentrations of various elements in the leachate from the column were measured during the experiment. Also, the remnants within the soil were measured at the end of the experiment. The geochemical modelling package, PHREEQC-2, was used to simulate the experimental data. Processes included in the CCA transport modelling were advection, dispersion, non-specific adsorption (cation exchange) and specific adsorption by clay minerals and organic matter, as well as other possible chemical reactions such as precipitation/dissolution. The modelling effort highlighted the possible complexities in CCA transport and reaction experiments. For example, the uneven dosing of CCA as well as incomplete knowledge of the soil properties resulted in simulations that gave only partial, although reasonable, agreement with the experimental data. Both the experimental data and simulations show that As and Cu are strongly adsorbed and therefore, will mostly remain at the top of the soil profile, with a small proportion appearing in leachate. On the other hand, Cr is more mobile and thus it is present in the soil column leachate. Further simulations show that both the quantity of CCA added to the soil and the pH of the irrigation water will influence CCA transport. Simulations suggest that application of larger doses of CCA to the soil will result in higher leachate concentrations, especially for Cu and As. Irrigation water with a lower pH will dramatically increase leaching of Cu. These results indicate that acidic rainfall or significant accidental spillage of CCA will increase the risk of groundwater pollution.

A review of long-distance movements by marine turtles, and the possible role of ocean currents

Sea turtle movements often occur in open-sea unsheltered areas, and are therefore likely to be influenced by major oceanographic processes. Only recently has work started to examine the possible relationships of these movements with dynamic oceanic features, and consequently a clear picture of such interaction is only available in a few cases. Newborn sea turtles are thought to rely on oceanic currents to reach their pelagic nursery habitats. The actual extent and timing of these developmental migrations are known for only a few populations, but these movements probably last several years and range over thousands of km. Large juveniles that have been tracked during their pelagic stage were found to make long-distance movements, sometimes swimming against the prevailing currents. Older juveniles of most species leave the pelagic habitat to recruit to neritic developmental habitats. This is a very poorly documented phase of the sea turtle life-cycle, and the few available indications show that turtles may have to swim actively for enormous distances to counterbalance their previous drift with the current. The course and extent of adult postnesting migrations vary greatly among different turtle species, but two main patterns are evident. Some species, like green, hawksbill and loggerhead turtles, shuttle between the nesting beach and a specific feeding area used for the entire inter-reproductive period. In these cases, individuals swim, rather than drift, to complete their journeys, with possible advection due to currents sometimes helping them to quickly reach their target, but sometimes providing navigational challenges. Other species such as the olive ridley and the leatherback turtle, leave the coastal nesting areas to reach the pelagic environment where they forage, and perform wandering movements. Major oceanographic processes (such as main currents and eddies) have been recently shown to have a remarkable influence on leatherback movements, making it questionable whether these journeys are to be considered migrations or, rather, prolonged stays in vast feeding areas.

Use of artificial neural networks as explicit finite difference operators

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5 , where C is the Courant number and P , the Peclet number (i.e., the restriction imposed by the Neumann stability condition).